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Student Researchers Selected for ALERT Professional Development Award November 27, 2017

November 27, 2017

Three ALERT student researchers have been selected to receive the first ALERT Professional Development Award in November 2017. The winners are Qi Feng, a Ph.D. student working with Prof. Stan Sclaroff at Boston University; Ashraful Islam, a Ph.D. student working with Prof. Richard Radke at Rensselaer Polytechnic Institute; and Abubakar Siddique, a Ph.D. student working with Prof. Henry Medeiros at Marquette University.

The ALERT Professional Development Award is intended to encourage ALERT students to participate in professional development activities throughout the year and to facilitate their future participation in networking and career development opportunities. ALERT selects up to three students each year to win a $1,500 stipend that can be used towards attendance at a professional or academic conference and/or to visit and collaborate with a lab related to their ALERT research project. This year, applications were accepted August through October 2017. More information about next year’s award cycle is forthcoming.

ADSA17 Presentations Now Available November 10, 2017

We are pleased to announce that the presentations from The Seventeenth Advanced Development for Security Applications Workshop (ADSA17) which was held on October 17-18, 2017 at Northeastern University in Boston, MA are now available for download. The presentations from the ADSA17 Workshop are now available at the following link:

The title of the workshop was, “Systems Engineering of Aviation Security Systems.” View all slides, as well as the reports from past ADSA workshops here.

If you have any questions regarding the topics and technologies discussed at the workshop, please contact ALERT at


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Student Spotlight: Elizabeth Wig September 27, 2017

Congratulations to Elizabeth Wig, a Northeastern University (NU) Electrical Engineering undergraduate conducting ALERT research, for receiving the Society of Women Engineers GE Women’s Network Scholarship! Elizabeth will receive this award, which comes with a $5,000 stipend, in October 2017 at the SWE Annual Conference in Austin, Texas. Elizabeth has been working with ALERT R3 Thrust Leader, Professor Carey Rappaport since Summer 2016, conducting research on “Computational Models & Algorithms for Millimeter Wave Whole Body Scanning for AIT,” in collaboration with Smiths Detection (Project R3-A.2). When asked how Elizabeth got involved with the project so early on in her undergraduate career, she explained that she met Professor Rappaport at a NU-sponsored ski event and found out about his research while riding up the mountain on a chairlift. However, Elizabeth explained that her interest in this research began much earlier, “When my high school physics class did its electricity and magnetism unit, the symmetry was strikingly beautiful. I loved the way relatively few equations could describe so much of what makes up our world, from why sunrises are so beautiful to the way molecules hold together to Wi-Fi.”  The aspect of her research that she is most passionate about is math, and learning about the different ways mathematics can be used to describe and explain our world. This fits in well with her role on the project, which involves developing the model used to detect and characterize potential explosives threats and eliminate false alarms using a millimeter-wave body scanner. She has been working to make and refine the model to improve the accuracy in characterization.

Beyond her recent award, Elizabeth has also published a paper on her work with Mahdiar Sadeghi, a Northeastern graduate student, and Professor Rappaport, and is currently working on her second paper. She and Mahdiar were also asked to present their work at the ADSA15 (Advanced Development for Security Applications) Workshop in November 2016.

Elizabeth has already gained valuable work experience through her Spring 2017 co-op position at Draper Laboratories in Cambridge, MA. There she worked on electrical engineering projects in their Sensors and Imaging Systems group. As for her future career, she hopes to continue her education and complete a Ph.D. program, and if possible, get the opportunity to travel more internationally and work with NASA!

Video: ALERT Undergrads Discuss their Research in Advanced Imaging Technology (AIT) September 27, 2017

ALERT Undergraduate Research in the AIT Lab at Northeastern University

Engineering undergraduates at Northeastern University (Jacob Londa, Daniel Castle, and Nikhil Phatak) describe their work on the ALERT AIT (Advanced Imaging Technology) project, which is led by ALERT Deputy Director, Prof. Carey Rappaport, a faculty member in the Electrical and Computer Engineering department at Northeastern.

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ALERT Phase 2 Year 4 Annual Report Available Online! September 27, 2017

ALERT is proud to announce that the Phase 2 Year 4 Annual Report is now available for download online. This report details the continued research in ALERT’s four thrusts:

  • R1 Characterization & Elimination of Illicit Explosives
  • R2 Trace & Vapor Sensors
  • R3 Bulk Sensors & Sensor Systems
  • R4 Video Analytics & Signature Analysis

A full bibliography of publications and presentations conducted under ALERT support follows the individual project reports. Comprehensive descriptions of the Year 4 activities that took place in our Research and Transition, Education, Strategic Studies, Safety, and Information Protection Programs, as well as the ALERT Phase 2 Overview and Year 4 Highlights, Infrastructure and Evaluation, and Industrial/Practitioner and Government Partnerships can also be accessed in the Annual Report.

DEADLINE EXTENDED TO 10/20: ALERT Professional Development Award September 22, 2017

ALERT Students, the deadline for the ALERT Professional Development Award has been extended to Friday, October 20, 2017. This award provides up to three students with financial support to attend a conference or visit an ALERT or DHS affiliated lab as part of their research experience.

Apply Today!

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Postdoctoral Position Available at Tufts University September 19, 2017

Applications are invited for a postdoctoral position in the Tufts Information and Networked Systems (TINS) lab in collaboration with the recently established Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University and the U.S. Army Natick Soldier Systems Center at Natick, MA. This appointment would be for 12-18 months with an estimated start date of October 2017.

The primary project is entitled “Real time prediction of individual and team performance metric from neurophysiological measurements and team interaction data.” Under this project, the fellow will work with Tufts ECE faculty, Dr. Shuchin Aeron and Dr. Eric Miller, as well as CABCS scientists to develop supervised and semi-supervised machine learning algorithms that are capable of predicting cognitive state (e.g., stress level and alertness) and task performance metrics (e.g., target identification and marksmanship) from a wide assortment of physiological sensor data (both labeled and unlabeled) including information collected continuously as a function of time (EEG, FNIRS, Heart Rate) as well as data at a relatively few points in time before, during, and after a specific task (saliva and urine samples).  In addition to assessing individuals, data will be collected to support the characterization of team and intergroup dynamics. We anticipate the effort will require the use of several classical as well as recent developments in machine learning and in particular recursive neural networks, manifold learning, and social network analysis.

While previous experience in theoretical and applied machine learning would be ideal, we welcome applicants with significant experience in related fields including information theory, statistical signal processing, sparse signal or image processing, compressive sensing, and distributed convex optimization.

Interested applicants should send a cover letter detailing their research interests and career goals, CV, and names and contact information of 3 references to Dr. Shuchin Aeron (

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New Video Analytics Dataset available for use August 25, 2017

ALERT Airport Re-Identification Dataset

As part of the ALERT video analytics effort, researchers at Northeastern University and Rensselaer Polytechnic Institute developed an annotated dataset that accurately reflects the real-world person re-identification problem. The dataset was constructed using video data from the six cameras installed post central security checkpoint at an active commercial airport within the United States. (No NDA required)

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Follow ALERT on Facebook August 24, 2017

ALERT recently joined Facebook! Follow us online and keep up to date on ALERT’s research and education programs, as well as upcoming events and opportunities. Search @alertcoe on Facebook for ALERT updates.
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Five Questions with Srikrishna Karanam (RPI, MS ’14, PhD ’17) July 28, 2017

Former ALERT student researcher, Srikrishna Karanam, reflects on his time with ALERT and how it prepared him for working in the Homeland Security Enterprise.

Srikrishna joined ALERT in 2013 as a graduate student working with Prof. Richard J. Radke at Rensselaer Polytechnic Institute (RPI) on video analytics problems in camera networks. At RPI, he earned his MS in Electrical Engineering and his Ph.D. in Computer and Systems Engineering. Srikrishna is now working as a Research Scientist at Siemens Corporate Research, focusing on computer vision and machine learning.

What professional development opportunities, aside from research experience, benefitted you during your time as an ALERT student?

SK:  During my time as an ALERT student, I attended several major conferences in Computer Vision – CVPR 2015 in Boston, MA, BMVC 2015 in Swansea, UK and ICCV 2015 in Santiago, Chile.  Going to these conferences allowed me to discuss open problems and establish connections with several researchers in my field. Furthermore, I participated in several ALERT events – ASPIRE, ADSA, and ALERT annual meetings – where I got opportunities to present my work to several stakeholders in the security and surveillance industry.

These ALERT events were crucial in that they helped me focus my algorithmic and systems research on operational aspects from an end-user’s perspective – I believe these are critical issues as we transition laboratory research into working prototypes in the real world. 

We understand that you were working under the supervision of former ALERT student, Ziyan Wu (RPI, PhD ‘14) during an internship with Siemens Corporate Research (Princeton, NJ) last year. What were some highlights from that experience?

SK:  I was given a lot of independence in addressing existing problems the group at Siemens was tackling. This gave me an opportunity to explore several algorithmic as well as implementation and engineering components of the project I was assigned to. At the algorithmic level, I developed new algorithms and demonstrated improved performance on internal datasets. In addition, I assisted the group in integrating these algorithms as part of a large system that has been deployed for in-the-field testing.

This assignment provided me with valuable, real-world, hands-on research experience. Ziyan and others in the Vision Technologies and Solutions group were very supportive, kind, and welcoming, and I thoroughly enjoyed working there and developed great friendships along the way. 

During your time at ALERT, you collaborated with ALERT teams from RPI, Northeastern University, and Boston University. Can you tell us a little bit about these collaborations and how they have prepared you to work in industry? Have you continued these collaborations post-graduation?

SK:  I worked with the ALERT teams from RPI, Northeastern University, and Boston University on the VAST “Tag and Track” project (see related video at: for over 3 years.  Each team was responsible for specific parts of the project, with the goal of deploying and testing a working prototype of the system at the Cleveland International Airport, which was successfully achieved in Summer 2015.

The “Tag and Track” project provided me with real-world research, development, and project management experience, helping develop skills that are particularly relevant to industrial research labs. At Siemens Corporate Technology, I have been working on solving vision problems with practical relevance across multiple industrial units, and my experience with ALERT has helped me transition into my current work environment seamlessly.

Because of this project, I developed close collaborations (and friendships) with several researchers from Northeastern (specifically, Mengran Gou (NU, PhD ’17) and Oliver Lehmann (NU, PhD ’15)) in addition to Ziyan Wu and Austin Li (RPI, PhD ’15) from RPI. For instance, since the winter of 2015, Mengran and I have been closely collaborating on a project where our goal is to benchmark the current state-of-the-art in person re-identification for the convenience of the larger research community – as part of this work, we have evaluated several hundreds of different algorithms on numerous public datasets. Ziyan and I have been closely working together on numerous problems for many years – initially at RPI and now at Siemens.

Can you describe your role at Siemens and the research you are conducting now?

SK:  I work as a Research Scientist in the Vision Technologies and Solutions group at Siemens Corporate Technology, where I research topics in Computer Vision and Machine Learning. I am responsible for developing algorithms to address research problems, as well as prototype systems that leverage these algorithms to solve real-world problems. My current research focuses on all aspects of image indexing, search, and retrieval with applications in object recognition and pose estimation.

Where do you see yourself in 5 years?

SK:  My past research experience at RPI and ALERT has made me realize the importance of, and challenges in, getting lab-optimized research to work effectively in the “wild” real-world. To this end, I hope to contribute towards bridging this “gap,” enabling and building systems that offer Computer Vision, Machine Learning, and Data Analytics technologies as services to solve a wide variety of real-world problems.